I recently found a dataset on @Hugging Face that has a huge list of prompts. It's called awesome-chatgpt-prompts, but they point out that it's applicable to other models as well. There are some really useful ones in it along with some funny ones like Magician and Lunatic

I've found persona prompts like this to be really helpful, and I want to start trying out some of these now. I've even gotten my wife to start using some persona prompts in @ChatGPT by OpenAI and she's getting better results.

I use Claude and Codex on the same codebase, and the handoff matters more to me than raw coding speed. A second agent can catch a mistake, but only if it checks the work independently.

My split: Claude writes the brief and reviews; Codex implements in its own branch, then Claude reruns the tests.

If you use two agents, how do you divide the work and decide a task is ready to ship?

Today, traditional engineering interviews often revolve around DSA (Data Structures and Algorithms).
And while DSA tests analytical rigor, it also wires thinking into strict, logical frames.

Creativity lives outside those frames.
Problem-solving and creating experiences are two entirely different games.
And sometimes, forcing a purely analytical mindset can quietly erode creative instincts the very instincts vibe coders thrive on.

Which raises a bigger question:

Is the future of technology moving into the hands of more imaginative, creative builders rather than traditional analytical problem-solvers?

I keep coming back to this idea: vibe code is basically instant legacy code.

Not because AI-generated code is necessarily bad, but because it often puts us in a familiar situation: the system works, yet no one fully understands how it works. That makes me wonder whether some of the most useful techniques for vibe coding are actually techniques we already use for legacy systems. For example:

• Snapshot / approval tests: capture the current output and require explicit approval when it changes.

• Characterization tests: describe what the system actually does today, regardless of what it was originally intended to do.

• Golden master tests: run known inputs through the system and compare the results with a trusted baseline.

• Contract test: protect the boundaries between components, services, APIs, events, and data stores.

• Differential testing: compare two implementations and detect where their behavior diverges.

• Test carving: observe a larger execution and extract smaller, focused regression tests from it.

Currently playing around with @bolt.new after being inspired by @gabe and building a bunch of mini apps, mainly for fun. Haven't pushed any to production yet but so far I'm enjoying it bar the odd hiccup where I have to roll back a few times to fix a stubborn error.

I've also looked into @Lovable and it seems pretty cool but wanted to see what people had to say about it. Is there any reason to pick one over the other and which one have you settled on?

I've been having a lot of fun exploring AI and using tools like @Cursor, @bolt.new, @Lovable, and @Warp to learn how to build and make some apps for myself! I'm also noticing a tremendous amount of growth in folks creating their own apps using these same tools which has me wondering... if a company wanted to acquire someone's app or tool that was built via vibe coding, would it matter how it was built? Does the method of how it was built impact the valuation?
In my idealistic eyes, I'd like to think it doesn't. As an acquisition is often much more than just the tech but also the user base, brand, and even team behind the product. If anything I think that acquiring a product that has been "vibe coded" and putting them into capable engineering hands would only enhance the product...or a least make the code base cleaner.
I also believe that talent that is able to create stunning products with AI is currently a small percentage of folks, and that companies should be investing in acquiring that talent (either independently or via product acquisition) so that they can stay ahead in innovation while learning how to implement AI tools more efficiently in their orgs.
Very curious to hear what you all think!

After rebuilding the same project three times because AI forgot my architecture, I got fed up and built @CodeRide (Beta) with my team.

The problem: AI code assistants lose track of your project between sessions. Every time I start coding with Cursor, Claude, or any AI assistant, I waste time re-explaining my codebase structure, architectural decisions, and coding patterns.

What we built: The project management tool for coding agents using MCP. Upload your project documentation or PRD, and CodeRide breaks it into optimized, fully contextual tasks ready for your AI agent.

Mine was trusting the diff summary instead of actually reading the diff.

I used to just glance at "renamed X, updated Y files, tests passing" and move on. Then one day the agent quietly changed a timeout value in a config file while it was "cleaning up," totally unrelated to what I asked for. Nothing broke locally, so it shipped. Took three days in prod before anyone noticed requests were timing out way faster than before.

Lately I've noticed most discussion around AI coding tools focuses on which assistant or terminal agent people use, but not much on the actual architecture decisions behind that, things like framework, hosting, database, and auth choices that affect cost and security down the line.

For context, my current default stack is Next.js on the frontend, Supabase for database and auth, Vercel for hosting, and Stripe for payments. I picked it mainly for speed of shipping and because it keeps infra costs predictable early on, but I'm not sure it's the best call for security or scaling past MVP stage.

A few things I'm curious about:

• Do you settle on a default stack before prompting your AI agent, or does the agent end up influencing your stack choice?

• What's been your biggest regret, cost, security, or scaling wise, with a stack you picked early on?

• Any stack you'd actively avoid for a new SaaS in 2026?